fix: whisper transcription compatibility with newer transformers (#274)

- Use getattr for max_length to handle removed WhisperConfig attribute
- Cast input_features to model dtype to fix float16 mismatch

.fdignore

@@ -0,0 +1,7 @@
+**/GFPGAN/inputs/**
+**/GFPGAN/tests/**
+**/frame_interpolation/photos/*
+moment.gif
+node.zip
+.DS_Store
+

.github/workflows/publish_action.yml

@@ -1,9 +1,6 @@
 name: 📦 Publish to Comfy registry
 on:
   workflow_dispatch:
-  push:
-    tags:
-      - '*'
 
 permissions:
   issues: write
@@ -21,4 +18,5 @@ jobs:
       - name: 📦 Publish Custom Node
         uses: Comfy-Org/publish-node-action@v1
         with:
+          skip_checkout: 'true'
           personal_access_token: ${{ secrets.COMFY_REGISTRY_TOKEN }}

.gitignore

@@ -1,11 +1,16 @@
 __pycache__
 *.py[cod]
 *.onnx
+
 wheels/
 node_modules/
 compose.yaml
 comfy_mtb.wsb
 Dockerfile
 
+.DS_Store
+node.zip
+
 # I store the gh-pages worktrees (src & build) there
 .worktrees
+comfy.lock

README.md

@@ -1,4 +1,10 @@
 # MTB Nodes
+
+> [!NOTE]
+> master/main is outdated for now to keep backward compatibility, the next version is being worked on in
+> [`dev/0.6.0`](https://github.com/melMass/comfy_mtb/tree/dev/0.6.0)
+
+
 [![embedded test](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml/badge.svg)](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml)
 
 ![home](https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873)

__init__.py

@@ -3,11 +3,11 @@
 # File: __init__.py
 # Project: comfy_mtb
 # Author: Mel Massadian
-# Copyright (c) 2023 Mel Massadian
+# Copyright (c) 2023-2025 Mel Massadian
 #
 ###
 
-__version__ = "0.3.0"
+__version__ = "0.5.4"
 
 import os
 
@@ -34,6 +34,8 @@ from aiohttp import web
 
 IN_COMFY = False
 
+PromptServer = None
+
 try:
     from server import PromptServer
 
@@ -75,7 +77,7 @@ def extract_nodes_from_source(filename: Path):
                             )
                         break
     except SyntaxError:
-        log.error("Failed to parse")
+        log.error(f"Failed to parse ast from: {filename}")
     return nodes
 
 
@@ -240,10 +242,29 @@ if failed:
 # - ENDPOINT
 
 
-if IN_COMFY and hasattr(PromptServer, "instance"):
+# TODO: move that away and simplify existing endpoints
+
+
+def register_routes():
+    if not PromptServer:
+        log.error("No prompt server, are you inside comfy?")
+
+    if PromptServer.instance.app.frozen:
+        log.warning(
+            "The router is frozen and cannot be further edited."
+            "If you are hot reloading mtb this is expected."
+        )
+        return
+
     img_cache = None
     prompt_cache = None
 
+    import asyncio
+    import os
+    from io import BytesIO
+
+    from PIL import Image
+
     with contextlib.suppress(ImportError):
         from cachetools import TTLCache
 
@@ -360,13 +381,6 @@ if IN_COMFY and hasattr(PromptServer, "instance"):
         # Return JSON for other requests
         return web.json_response({"message": "Welcome to MTB!"})
 
-    import asyncio
-    import os
-    from io import BytesIO
-
-    from aiohttp import web
-    from PIL import Image
-
     def get_cached_image(file_path: str, preview_params=None, channel=None):
         cache_key = (file_path, preview_params, channel)
         if img_cache and (cache_key in img_cache):
@@ -571,6 +585,10 @@ if IN_COMFY and hasattr(PromptServer, "instance"):
         return await endpoint.do_action(request)
 
 
+if IN_COMFY and hasattr(PromptServer, "instance"):
+    register_routes()
+
+
 # - WAS Dictionary
 MANIFEST = {
     "name": "MTB Nodes",  # The title that will be displayed on Node Class menu,. and Node Class view

biome.json

@@ -1,19 +1,26 @@
 {
-  "$schema": "https://biomejs.dev/schemas/1.6.1/schema.json",
-  "organizeImports": {
-    "enabled": true
-  },
+  "$schema": "https://biomejs.dev/schemas/2.0.5/schema.json",
+  "assist": { "actions": { "source": { "organizeImports": "on" } } },
   "linter": {
     "enabled": true,
     "rules": {
       "recommended": true,
       "suspicious": {
-        "noConsoleLog": "warn"
+        "noConsole": { "level": "warn", "options": { "allow": ["log"] } }
       },
       "style": {
         "noParameterAssign": "off",
         "noShoutyConstants": "warn",
-        "useNamingConvention": "off"
+        "useNamingConvention": "off",
+        "useAsConstAssertion": "error",
+        "useDefaultParameterLast": "error",
+        "useEnumInitializers": "error",
+        "useSelfClosingElements": "error",
+        "useSingleVarDeclarator": "error",
+        "noUnusedTemplateLiteral": "error",
+        "useNumberNamespace": "error",
+        "noInferrableTypes": "error",
+        "noUselessElse": "error"
       }
     }
   },

endpoint.py

@@ -15,7 +15,6 @@ from .utils import (
     backup_file,
     build_glob_patterns,
     glob_multiple,
-    import_install,
     reqs_map,
     run_command,
     styles_dir,
@@ -24,7 +23,6 @@ from .utils import (
 endlog = mklog("mtb endpoint")
 
 # - ACTIONS
-import_install("requirements")
 
 
 def ACTIONS_installDependency(dependency_names: list[str] | None = None):
@@ -112,11 +110,15 @@ def ACTIONS_getUserVideos(
 
 def ACTIONS_getUserImages(
     mode: Literal["input", "output"],
+    target_width: int | str | None = None,
     count=1000,
     offset=0,
     sort: str | None = None,
     include_subfolders: bool = False,
-    subfolder=None,
+    subfolder: str | None = None,
+    # IIRC I copied this from Comfy base
+    # just keeping it until I properly checked implications
+    salt_urls=False,
 ):
     # enabled = "MTB_EXPOSE" in os.environ
     # if not enabled:
@@ -124,11 +126,12 @@ def ACTIONS_getUserImages(
 
     imgs = {}
     count = count or 1000
+    target_width = int(target_width) if target_width else None
 
     input_dir = Path(folder_paths.get_input_directory())
     output_dir = Path(folder_paths.get_output_directory())
 
-    entry_dir = input_dir if mode == "input" else output_dir
+    entry_dir: Path = input_dir if mode == "input" else output_dir
     if subfolder:
         entry_dir = entry_dir / subfolder
 
@@ -157,9 +160,9 @@ def ACTIONS_getUserImages(
 
     imgs = {
         img.name: (
-            f"/mtb/view?filename={img.name}&width=512&type={mode}&subfolder={subfolder or ''}"
+            f"/mtb/view?filename={img.name}{f'&width={target_width}' if target_width and target_width > 0 else ''}&type={mode}&subfolder={subfolder or ''}"
             f"{img.parent.relative_to(entry_dir) if include_subfolders else ''}"
-            f"&preview=&rand={secrets.randbelow(424242)}"
+            f"&preview={f'&rand={secrets.randbelow(424242)}' if salt_urls else ''}"
         )
         for i, img in enumerate(entries)
         if offset <= i < offset + count

install.py

@@ -43,7 +43,6 @@ pip_map = {
     "tb-nightly": "tensorboard",
     "protobuf": "google.protobuf",
     "qrcode[pil]": "qrcode",
-    "requirements-parser": "requirements",
     # Add more mappings as needed
 }
 

nodes/audio.py

@@ -1,20 +1,16 @@
-from typing import Any, TypedDict
+from typing import TYPE_CHECKING, Any, TypedDict
 
 import torch
 import torchaudio
 from comfy.model_management import get_torch_device
 from huggingface_hub import snapshot_download
-from transformers import (
-    WhisperForConditionalGeneration,
-    WhisperProcessor,
-)
 
-# from transformers import (
-#     AutoFeatureExtractor,
-#     WhisperForConditionalGeneration,
-#     WhisperModel,
-#     WhisperProcessor,
-# )
+if TYPE_CHECKING:
+    from transformers import (
+        WhisperForConditionalGeneration,
+        WhisperProcessor,
+    )
+
 from ..log import log
 from ..utils import get_model_path
 
@@ -101,8 +97,8 @@ class MtbAudio:
 class WhisperPipeline(TypedDict):
     """Whisper model pipeline."""
 
-    processor: WhisperProcessor
-    model: WhisperForConditionalGeneration
+    processor: "WhisperProcessor"
+    model: "WhisperForConditionalGeneration"
 
 
 class MTB_LoadWhisper:
@@ -148,6 +144,11 @@ class MTB_LoadWhisper:
 
     def load(self, model_size="tiny", download_missing=False):
         """Load Whisper model and processor."""
+        from transformers import (
+            WhisperForConditionalGeneration,
+            WhisperProcessor,
+        )
+
         whisper_dir = get_model_path("whisper")
         tag = f"whisper-{model_size}"
         model_dir = whisper_dir / tag
@@ -276,14 +277,14 @@ class MTB_AudioToText(MtbAudio):
                 f"Processing chunk {chunk_offset:.1f}s - {chunk_end / sample_rate:.1f}s"
             )
 
-            max_length = model.config.max_length or 448
+            max_length = getattr(model.config, "max_length", None) or 448
             attention_mask = torch.ones((1, max_length))
 
             input_features = processor(
                 chunk_waveform,
                 sampling_rate=sample_rate,
                 return_tensors="pt",
-            ).input_features.to(device)
+            ).input_features.to(device=device, dtype=model.dtype)
 
             with torch.no_grad():
                 predicted_ids = model.generate(

nodes/batch.py

@@ -335,9 +335,9 @@ class MTB_BatchShape:
                 "image_width": ("INT", {"default": 512}),
                 "image_height": ("INT", {"default": 512}),
                 "shape_size": ("INT", {"default": 100}),
-                "color": ("COLOR", {"default": "#ffffff"}),
-                "bg_color": ("COLOR", {"default": "#000000"}),
-                "shade_color": ("COLOR", {"default": "#000000"}),
+                "color": ("COLOR", {"default": "#ffffff","widgetType": "MTB_COLOR"}),
+                "bg_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
+                "shade_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
                 "thickness": ("INT", {"default": 5}),
                 "shadex": ("FLOAT", {"default": 0.0}),
                 "shadey": ("FLOAT", {"default": 0.0}),
@@ -842,7 +842,7 @@ class MTB_Batch2dTransform:
                     ["edge", "constant", "reflect", "symmetric"],
                     {"default": "edge"},
                 ),
-                "constant_color": ("COLOR", {"default": "#000000"}),
+                "constant_color": ("COLOR", {"default": "#000000","widgetType": "MTB_COLOR"}),
             },
             "optional": {
                 "x": ("FLOATS",),

nodes/clock.py

@@ -0,0 +1,190 @@
+import time
+import uuid
+from collections import OrderedDict
+from typing import Any, TypedDict
+
+from comfy.comfy_types.node_typing import IO as CIO
+from server import PromptServer
+
+from ..log import log
+
+
+class Clock(TypedDict):
+    name: str
+    start: float
+    end: float | None
+
+
+active_timers: OrderedDict[str, Clock] = OrderedDict()
+
+# TODO: lower this
+MAX_CLOCKS = 50
+
+
+class MTB_StartClock:
+    """
+    Starts a profiling clock with a given name.
+
+    Outputs a unique ID that must be passed to EndClock.
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Clock A"}),
+                "cache": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Cache the clock ID, this means the node will follow Comfy's default invalidation system. If False it will always invalidate / mark the node as 'dirty'",
+                    },
+                ),
+            },
+            "optional": {
+                "passthrough": (CIO.ANY,),
+            },
+        }
+
+    RETURN_TYPES = (
+        CIO.ANY,
+        "STRING",
+    )
+    RETURN_NAMES = (
+        "passthrough",
+        "clock_id",
+    )
+    FUNCTION = "start_timer"
+    CATEGORY = "mtb/utils"
+
+    def start_timer(
+        self, *, name: str, passthrough: Any | None = None, **kwargs
+    ):
+        global active_timers
+
+        if len(active_timers) >= MAX_CLOCKS:
+            # get oldest clock
+            removed_key = None
+            for key, clock_data in active_timers.items():
+                if clock_data["end"] is not None:
+                    removed_key = key
+                    break
+            if removed_key:
+                removed_clock = active_timers.pop(removed_key)
+                log.info(
+                    f"[Profiling] Evicted finished clock '{removed_clock['name']}' (ID: {removed_key}) due to limit ({MAX_CLOCKS})."
+                )
+            else:
+                removed_key, removed_clock = active_timers.popitem(last=False)
+                log.warning(
+                    f"[Profiling] Evicted running clock '{removed_clock['name']}' (ID: {removed_key}) due to limit ({MAX_CLOCKS})."
+                )
+
+        clock_id = str(uuid.uuid4())
+        start_time = time.perf_counter()
+
+        active_timers[clock_id] = {
+            "start": start_time,
+            "name": name,
+            "end": None,
+        }
+
+        active_timers.move_to_end(clock_id)
+
+        log.debug(f"[Profiling] Clock '{name}' (ID: {clock_id}) started.")
+
+        return (
+            passthrough,
+            clock_id,
+        )
+
+    @classmethod
+    def IS_CHANGED(
+        cls, *, name: str, cache: bool = False, passthrough: Any | None = None
+    ):
+        if not cache:
+            return float("Nan")
+
+        return {"name": name, "cache": cache, "passthrough": passthrough}
+
+
+class MTB_EndClock:
+    """
+    Stops a profiling clock identified by its ID and returns the elapsed time in milliseconds.
+
+    Errors if the clock ID is not found or already stopped.
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "clock_id": (
+                    "STRING",
+                    {"forceInput": True},
+                ),
+            },
+            "optional": {
+                "passthrough": (CIO.ANY,),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = (
+        CIO.ANY,
+        "STRING",
+        "FLOAT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "passthrough",
+        "name",
+        "seconds",
+        "milliseconds",
+    )
+    FUNCTION = "end_timer"
+    CATEGORY = "mtb/utils"
+
+    def end_timer(self, clock_id: str, passthrough, unique_id=None):
+        global active_timers
+
+        if clock_id not in active_timers:
+            raise ValueError(
+                f"Error: Clock with ID '{clock_id}' not found. "
+                "Ensure StartClock was executed for this ID and proper passthrough chaining."
+            )
+
+        clock = active_timers[clock_id]
+        if clock.get("end") is not None:
+            return (passthrough, clock["name"], clock["end"])
+
+        start_time = clock["start"]
+        end_time = time.perf_counter()
+
+        duration_seconds = end_time - start_time
+        duration_ms = int(duration_seconds * 1000)
+        clock["end"] = duration_ms
+
+        active_timers.move_to_end(clock_id)
+
+        log.debug(
+            f"[Profiling] Clock '{clock['name']}' (ID: {clock_id}) stopped. Elapsed: {duration_ms}ms"
+        )
+        if unique_id:
+            PromptServer.instance.send_progress_text(
+                f"Clock '{clock['name']}' took {duration_seconds:.4f} seconds",
+                unique_id,
+            )
+
+        return (passthrough, clock["name"], duration_seconds, duration_ms)
+
+
+__nodes__ = [MTB_StartClock, MTB_EndClock]

nodes/crop.py

@@ -1,13 +1,22 @@
-import numpy as np
+from typing import NamedTuple
+
 import torch
-from PIL import Image, ImageDraw, ImageFilter
+import torchvision.transforms.functional as TF
 
 from ..log import log
-from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
+
+
+class BoundingBox(NamedTuple):
+    """The bounding box tuple."""
+
+    x: int
+    y: int
+    width: int
+    height: int
 
 
 class MTB_Bbox:
-    """The bounding box (BBOX) custom type used by other nodes"""
+    """A literal bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -37,12 +46,14 @@ class MTB_Bbox:
     FUNCTION = "do_crop"
     CATEGORY = "mtb/crop"
 
-    def do_crop(self, x: int, y: int, width: int, height: int):  # bbox
-        return ((x, y, width, height),)
+    def do_crop(
+        self, x: int, y: int, width: int, height: int
+    ) -> tuple[BoundingBox]:  # bbox
+        return (BoundingBox(x, y, width, height),)
 
 
 class MTB_SplitBbox:
-    """Split the components of a bbox"""
+    """Split the components of a bbox."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -55,8 +66,8 @@ class MTB_SplitBbox:
     RETURN_TYPES = ("INT", "INT", "INT", "INT")
     RETURN_NAMES = ("x", "y", "width", "height")
 
-    def split_bbox(self, bbox):
-        return (bbox[0], bbox[1], bbox[2], bbox[3])
+    def split_bbox(self, bbox: BoundingBox) -> BoundingBox:
+        return bbox
 
 
 class MTB_UpscaleBboxBy:
@@ -74,26 +85,23 @@ class MTB_UpscaleBboxBy:
 
     FUNCTION = "upscale"
 
-    def upscale(
-        self, bbox: tuple[int, int, int, int], scale: float
-    ) -> tuple[tuple[int, int, int, int]]:
+    def upscale(self, bbox: BoundingBox, scale: float) -> tuple[BoundingBox]:
         x, y, width, height = bbox
 
-        center_x = x + width // 2
-        center_y = y + height // 2
+        center_x = x + width / 2
+        center_y = y + height / 2
 
         new_width = int(width * scale)
         new_height = int(height * scale)
 
-        new_x = center_x - new_width // 2
-        new_y = center_y - new_height // 2
+        new_x = int(center_x - new_width / 2)
+        new_y = int(center_y - new_height / 2)
 
-        scaled = (new_x, new_y, new_width, new_height)
-        return (scaled,)
+        return (BoundingBox(new_x, new_y, new_width, new_height),)
 
 
 class MTB_BboxFromMask:
-    """From a mask extract the bounding box"""
+    """From a mask extract the bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -103,7 +111,7 @@ class MTB_BboxFromMask:
                 "invert": ("BOOLEAN", {"default": False}),
             },
             "optional": {
-                "image": ("IMAGE",),
+                "image": ("IMAGE", {"tooltip": "Optional image"}),
             },
         }
 
@@ -119,52 +127,44 @@ class MTB_BboxFromMask:
     CATEGORY = "mtb/crop"
 
     def extract_bounding_box(
-        self, mask: torch.Tensor, invert: bool, image=None
-    ):
-        # if image != None:
-        #     if mask.size(0) != image.size(0):
-        #         if mask.size(0) != 1:
-        #             log.error(
-        #                 f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
-        #             )
+        self,
+        mask: torch.Tensor,
+        *,
+        invert: bool = False,
+        image: torch.Tensor | None = None,
+    ) -> tuple[BoundingBox, torch.Tensor | None]:
+        mask = 1 - mask if invert else mask
+        non_zero_indices = torch.nonzero(mask)
 
-        #             raise Exception(
-        #                 f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
-        #             )
+        if non_zero_indices.numel() == 0:
+            log.warning(
+                "BboxFromMask: Mask is empty. Returning a (0,0,0,0) bbox."
+            )
+            return (BoundingBox(0, 0, 0, 0), image)
 
-        # we invert it
-        _mask = tensor2pil(1.0 - mask)[0] if invert else tensor2pil(mask)[0]
-        alpha_channel = np.array(_mask)
+        min_coords = torch.min(non_zero_indices, dim=0).values
+        max_coords = torch.max(non_zero_indices, dim=0).values
 
-        non_zero_indices = np.nonzero(alpha_channel)
+        min_y, min_x = min_coords[1].item(), min_coords[2].item()
+        max_y, max_x = max_coords[1].item(), max_coords[2].item()
 
-        min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-        min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+        width = max_x - min_x + 1
+        height = max_y - min_y + 1
 
-        # Create a bounding box tuple
-        if image != None:
-            # Convert the image to a NumPy array
-            imgs = tensor2np(image)
-            out = []
-            for img in imgs:
-                # Crop the image from the bounding box
-                img = img[min_y:max_y, min_x:max_x, :]
-                log.debug(f"Cropped image to shape {img.shape}")
-                out.append(img)
-
-            image = np2tensor(out)
-            log.debug(f"Cropped images shape: {image.shape}")
-        bounding_box = (min_x, min_y, max_x - min_x, max_y - min_y)
-        return (
-            bounding_box,
-            image,
+        bounding_box = BoundingBox(
+            int(min_x), int(min_y), int(width), int(height)
         )
 
+        cropped_image = None
+        if image is not None:
+            cropped_image = image[:, min_y : max_y + 1, min_x : max_x + 1, :]
+
+        return (bounding_box, cropped_image)
+
 
 class MTB_Crop:
-    """Crops an image and an optional mask to a given bounding box
+    """Crop an image and an optional mask to a given bounding box.
 
-    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
     The BBOX input takes precedence over the tuple input
     """
 
@@ -204,35 +204,38 @@ class MTB_Crop:
     def do_crop(
         self,
         image: torch.Tensor,
-        mask=None,
-        x=0,
-        y=0,
-        width=256,
-        height=256,
-        bbox=None,
+        *,
+        mask: torch.Tensor | None = None,
+        x: int = 0,
+        y: int = 0,
+        width: int = 256,
+        height: int = 256,
+        bbox: BoundingBox | None = None,
     ):
-        image = image.numpy()
-        if mask is not None:
-            mask = mask.numpy()
-
         if bbox is not None:
             x, y, width, height = bbox
 
-        cropped_image = image[:, y : y + height, x : x + width, :]
-        cropped_mask = None
-        if mask is not None:
-            cropped_mask = (
-                mask[:, y : y + height, x : x + width]
-                if mask is not None
-                else None
+        if width <= 0 or height <= 0:
+            log.error(
+                "Crop dimensions must be positive. Check the BBOX or widget inputs."
             )
-        crop_data = (x, y, width, height)
+            return (
+                torch.zeros_like(image),
+                torch.zeros_like(mask) if mask is not None else None,
+                (x, y, width, height),
+            )
+
+        cropped_image = image[:, y : y + height, x : x + width, :]
+        cropped_mask = (
+            mask[:, y : y + height, x : x + width]
+            if mask is not None
+            else None
+        )
+        crop_data = BoundingBox(x, y, width, height)
 
         return (
-            torch.from_numpy(cropped_image),
-            torch.from_numpy(cropped_mask)
-            if cropped_mask is not None
-            else None,
+            cropped_image,
+            cropped_mask if cropped_mask is not None else None,
             crop_data,
         )
 
@@ -246,35 +249,33 @@ class MTB_Crop:
 #     return (x_left, y_top, x_right, y_bottom)
 
 
-def bbox_check(bbox, target_size=None):
+def bbox_check(bbox: BoundingBox, target_size: tuple[int, int] | None = None):
     if not target_size:
         return bbox
 
-    new_bbox = (
-        bbox[0],
-        bbox[1],
-        min(target_size[0] - bbox[0], bbox[2]),
-        min(target_size[1] - bbox[1], bbox[3]),
+    new_bbox = BoundingBox(
+        bbox.x,
+        bbox.y,
+        min(target_size[0] - bbox.x, bbox.width),
+        min(target_size[1] - bbox.y, bbox.height),
     )
     if new_bbox != bbox:
-        log.warn(f"BBox too big, constrained to {new_bbox}")
+        log.warning(f"BBox too big, constrained to {new_bbox}")
 
     return new_bbox
 
 
-def bbox_to_region(bbox, target_size=None):
+def bbox_to_region(
+    bbox: BoundingBox, target_size: tuple[int, int] | None = None
+):
     bbox = bbox_check(bbox, target_size)
 
     # to region
-    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
+    return (bbox.x, bbox.y, bbox.x + bbox.width, bbox.y + bbox.height)
 
 
 class MTB_Uncrop:
-    """Uncrops an image to a given bounding box
-
-    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
-    The BBOX input takes precedence over the tuple input
-    """
+    """Uncrop an image to a given bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -291,91 +292,113 @@ class MTB_Uncrop:
         }
 
     RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "do_crop"
-
+    FUNCTION = "do_uncrop"
     CATEGORY = "mtb/crop"
 
-    def do_crop(self, image, crop_image, bbox, border_blending):
-        def inset_border(image, border_width=20, border_color=(0)):
-            width, height = image.size
-            bordered_image = Image.new(
-                image.mode, (width, height), border_color
+    def do_uncrop(
+        self,
+        image: torch.Tensor,
+        crop_image: torch.Tensor,
+        bbox: BoundingBox,
+        border_blending: float = 0.25,
+    ):
+        if len(image) > 1 and len(image) != len(crop_image):
+            raise ValueError(
+                "Uncrop: Batch size of background 'image' must be 1 or match the 'crop_image' batch size."
             )
-            bordered_image.paste(image, (0, 0))
-            draw = ImageDraw.Draw(bordered_image)
-            draw.rectangle(
-                (0, 0, width - 1, height - 1),
-                outline=border_color,
-                width=border_width,
-            )
-            return bordered_image
+        import comfy.utils
 
-        single = image.size(0) == 1
-        if image.size(0) != crop_image.size(0):
-            if not single:
-                raise ValueError(
-                    "The Image batch count is greater than 1, but doesn't match the crop_image batch count. If using batches they should either match or only crop_image must be greater than 1"
-                )
+        pbar = comfy.utils.ProgressBar(4)
 
-        images = tensor2pil(image)
-        crop_imgs = tensor2pil(crop_image)
-        out_images = []
-        for i, crop in enumerate(crop_imgs):
-            if single:
-                img = images[0]
-            else:
-                img = images[i]
+        device = image.device
 
-            # uncrop the image based on the bounding box
-            bb_x, bb_y, bb_width, bb_height = bbox
+        log.debug(f"Working on device: {device}")
 
-            paste_region = bbox_to_region(
-                (bb_x, bb_y, bb_width, bb_height), img.size
-            )
-            # log.debug(f"Paste region: {paste_region}")
-            # new_region = adjust_paste_region(img.size, paste_region)
-            # log.debug(f"Adjusted paste region: {new_region}")
-            # # Check if the adjusted paste region is different from the original
+        crop_image = crop_image.to(device)
 
-            crop_img = crop.convert("RGB")
+        if len(image) == 1 and len(crop_image) > 1:
+            image = image.repeat(len(crop_image), 1, 1, 1)
 
-            log.debug(f"Crop image size: {crop_img.size}")
-            log.debug(f"Image size: {img.size}")
+        batch_size, bg_h, bg_w, _ = image.shape
+        _, fg_h, fg_w, _ = crop_image.shape
+        x, y, width, height = bbox
 
-            if border_blending > 1.0:
-                border_blending = 1.0
-            elif border_blending < 0.0:
-                border_blending = 0.0
-
-            blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
-
-            blend = img.convert("RGBA")
-            mask = Image.new("L", img.size, 0)
-
-            mask_block = Image.new("L", (bb_width, bb_height), 255)
-            mask_block = inset_border(mask_block, int(blend_ratio / 2), (0))
-
-            mask.paste(mask_block, paste_region)
-            log.debug(f"Blend size: {blend.size} | kind {blend.mode}")
-            log.debug(
-                f"Crop image size: {crop_img.size} | kind {crop_img.mode}"
-            )
-            log.debug(f"BBox: {paste_region}")
-            blend.paste(crop_img, paste_region)
-
-            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
-            mask = mask.filter(
-                ImageFilter.GaussianBlur(radius=blend_ratio / 4)
+        if (width, height) != (fg_w, fg_h):
+            log.warning(
+                f"Uncrop: crop_image size {(fg_w, fg_h)} "
+                "differs from bbox {(width, height)}. Resizing to fit bbox."
             )
 
-            blend.putalpha(mask)
-            img = Image.alpha_composite(img.convert("RGBA"), blend)
-            out_images.append(img.convert("RGB"))
+        resized_crop = crop_image.permute(0, 3, 1, 2)
+        resized_crop = torch.nn.functional.interpolate(
+            resized_crop,
+            size=(height, width),
+            mode="bicubic",
+            align_corners=False,
+        )
+        resized_crop = resized_crop.permute(0, 2, 3, 1)
 
-        return (pil2tensor(out_images),)
+        pbar.update(1)
+        # paste coords
+        paste_x1 = max(x, 0)
+        paste_y1 = max(y, 0)
+        paste_x2 = min(x + width, bg_w)
+        paste_y2 = min(y + height, bg_h)
+
+        # region from crop (bound)
+        crop_x1 = max(0, -x)
+        crop_y1 = max(0, -y)
+        crop_x2 = crop_x1 + (paste_x2 - paste_x1)
+        crop_y2 = crop_y1 + (paste_y2 - paste_y1)
+
+        if paste_x1 >= paste_x2 or paste_y1 >= paste_y2:
+            log.warning(
+                "Uncrop: BBOX is entirely outside the image boundaries. Returning original image."
+            )
+            return (image,)
+
+        pbar.update(1)
+        source_slice = resized_crop[:, crop_y1:crop_y2, crop_x1:crop_x2, :]
+
+        final_image = image.clone()
+        final_image[:, paste_y1:paste_y2, paste_x1:paste_x2, :] = source_slice
+
+        pbar.update(1)
+
+        blend_radius = int(max(width, height) * border_blending * 0.5)
+        if blend_radius > 0:
+            _device = device
+            if torch.cuda.is_available():
+                _device = torch.device("cuda")
+
+            log.debug("Processing blending")
+            alpha_mask = torch.zeros((batch_size, bg_h, bg_w), device=_device)
+            alpha_mask[:, paste_y1:paste_y2, paste_x1:paste_x2] = 1.0
+
+            kernel_size = 2 * blend_radius + 1
+
+            log.debug("Gaussian blur...")
+            alpha_mask = TF.gaussian_blur(
+                alpha_mask.unsqueeze(1), kernel_size=[kernel_size, kernel_size]
+            ).squeeze(1)
+            alpha_mask = alpha_mask.unsqueeze(-1)
+
+            log.debug("Applying blending")
+            final_image = final_image.to(_device) * alpha_mask + image.to(
+                _device
+            ) * (1.0 - alpha_mask)
+
+        pbar.update(1)
+        return (final_image.to(device),)
 
 
 class MTB_BBoxForceDimensions:
+    """
+    Resize a BBOX to new dimensions while keeping its center.
+
+    Optionally constrains the BBOX to stay within image boundaries.
+    """
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -383,6 +406,7 @@ class MTB_BBoxForceDimensions:
                 "bbox": ("BBOX",),
                 "width": ("INT", {"default": 512, "min": 1, "max": 8192}),
                 "height": ("INT", {"default": 512, "min": 1, "max": 8192}),
+                "constrain_to_image": ("BOOLEAN", {"default": True}),
             },
             "optional": {
                 "image": ("IMAGE",),
@@ -395,10 +419,12 @@ class MTB_BBoxForceDimensions:
 
     def force_dimensions(
         self,
+        *,
         bbox: tuple[int, int, int, int],
         width: int,
         height: int,
-        image: torch.Tensor = None,
+        constrain_to_image: bool = True,
+        image: torch.Tensor | None = None,
     ) -> tuple[tuple[int, int, int, int]]:
         x, y, curr_width, curr_height = bbox
 
@@ -408,27 +434,14 @@ class MTB_BBoxForceDimensions:
         new_x = center_x - width // 2
         new_y = center_y - height // 2
 
-        if image is not None:
+        if constrain_to_image and image is not None:
             img_height, img_width = image.shape[1:3]
-            x_overflow = max(0, new_x + width - img_width) + min(0, new_x)
-            y_overflow = max(0, new_y + height - img_height) + min(0, new_y)
-            if width > img_width or height > img_height:
-                x_exceed = width - img_width if width > img_width else 0
-                y_exceed = height - img_height if height > img_height else 0
-                raise ValueError(
-                    f"Target bbox dimensions ({width}x{height}) exceed image bounds ({img_width}x{img_height}) "
-                    f"by {x_exceed}px horizontally and {y_exceed}px vertically"
-                )
+            new_x = max(0, min(new_x, img_width - width))
+            new_y = max(0, min(new_y, img_height - height))
+            width = min(width, img_width)
+            height = min(height, img_height)
 
-            if x_overflow > 0 or x_overflow < 0:
-                new_x -= x_overflow
-
-            if y_overflow > 0:
-                new_y -= y_overflow
-            elif y_overflow < 0:
-                new_y -= y_overflow  # Add the negative overflow
-
-        return ((int(new_x), int(new_y), width, height),)
+        return ((new_x, new_y, width, height),)
 
 
 __nodes__ = [

nodes/faceswap.py

@@ -4,12 +4,8 @@ import sys
 from pathlib import Path
 
 import comfy.model_management as model_management
-import cv2
-import insightface
 import numpy as np
-import onnxruntime
 import torch
-from insightface.model_zoo.inswapper import INSwapper
 from PIL import Image
 
 from ..errors import ModelNotFound
@@ -43,6 +39,8 @@ class MTB_LoadFaceAnalysisModel:
     DEPRECATED = True
 
     def load_model(self, faceswap_model: str):
+        import insightface
+
         if faceswap_model == "antelopev2":
             download_antelopev2()
 
@@ -81,6 +79,9 @@ class MTB_LoadFaceSwapModel:
     DEPRECATED = True
 
     def load_model(self, faceswap_model: str):
+        import onnxruntime
+        from insightface.model_zoo.inswapper import INSwapper
+
         model_path = get_model_path("insightface", faceswap_model)
         if not model_path or not model_path.exists():
             raise ModelNotFound(f"{faceswap_model} ({model_path})")
@@ -212,6 +213,8 @@ def swap_face(
     face_swapper_model,
     faces_index: set[int] | None = None,
 ) -> Image.Image:
+    import cv2
+
     if faces_index is None:
         faces_index = {0}
     log.debug(f"Swapping faces: {faces_index}")

nodes/generate.py

@@ -193,11 +193,11 @@ by default it fallsback to a default font.
                 ),
                 "color": (
                     "COLOR",
-                    {"default": "black"},
+                    {"default": "black", "widgetType": "MTB_COLOR"},
                 ),
                 "background": (
                     "COLOR",
-                    {"default": "white"},
+                    {"default": "white", "widgetType": "MTB_COLOR"},
                 ),
                 "h_align": (("left", "center", "right"), {"default": "left"}),
                 "v_align": (("top", "center", "bottom"), {"default": "top"}),
@@ -343,9 +343,7 @@ by default it fallsback to a default font.
 
         def render_text(text_to_render, alpha=None):
             if trim:
-                text_to_render = (
-                    text_to_render.encode("ascii", "ignore").decode().strip()
-                )
+                text_to_render = text_to_render.strip()
             if wrap:
                 wrap_width = (((width / 100) * h_coverage) / font_size) * 2
                 lines = textwrap.wrap(text_to_render, width=wrap_width)
@@ -418,7 +416,9 @@ by default it fallsback to a default font.
                         active_chunks.append((chunk["text"], alpha))
 
                 for chunk_text, alpha in active_chunks:
-                    chunk_img = render_text(chunk_text, alpha)
+                    chunk_img = render_text(
+                        chunk_text.encode("ascii", "ignore").decode(), alpha
+                    )
                     frame = Image.alpha_composite(frame, chunk_img)
 
                 frames.append(frame)

nodes/graph_utils.py

@@ -4,11 +4,13 @@ import re
 import urllib.parse
 import urllib.request
 from math import pi
+from typing import Any
 
 import comfy.model_management as model_management
 import comfy.utils
 import numpy as np
 import torch
+from comfy.comfy_types.node_typing import IO as CIO
 from PIL import Image
 
 from ..log import log
@@ -867,6 +869,53 @@ class MTB_TensorOps:
         return (result,)
 
 
+class MTB_GetItem:
+    """Generic index based getter for common types"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "container": (CIO.ANY,),
+                "index": ("INT", {"default": 0}),
+            }
+        }
+
+    RETURN_TYPES = (CIO.ANY,)
+    RETURN_NAMES = ("item",)
+    FUNCTION = "get_item"
+    CATEGORY = "mtb/utils"
+
+    def get_item(self, container: Any, index: int):
+        if "__getitem__" in dir(container):
+            log.debug(f"Container is {type(container)}")
+            res = container[index]
+            if type(res) is torch.Tensor:
+                res = res.unsqueeze(0)
+
+            return (res,)
+
+
+class MTB_BooleanNot:
+    """Inverts a boolean."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bool_in": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("inverted_bool",)
+    FUNCTION = "invert"
+    CATEGORY = "mtb/utils"
+
+    def invert(self, bool_in: bool):
+        return (not bool_in,)
+
+
 __nodes__ = [
     MTB_StringReplace,
     MTB_FitNumber,
@@ -882,4 +931,6 @@ __nodes__ = [
     MTB_FloatToFloats,
     MTB_FloatsToInts,
     MTB_TensorOps,
+    MTB_BooleanNot,
+    MTB_GetItem,
 ]

nodes/image_processing.py

@@ -3,11 +3,12 @@ import json
 import math
 import os
 
-import comfy.model_management as model_management
+import comfy.utils
 import folder_paths
 import numpy as np
 import torch
 import torch.nn.functional as F
+from comfy import model_management
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from skimage.filters import gaussian
@@ -74,7 +75,10 @@ class MTB_ExtractCoordinatesFromImage:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "threshold": ("FLOAT",),
+                "threshold": (
+                    "FLOAT",
+                    {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
                 "max_points": ("INT", {"default": 50, "min": 0}),
             },
             "optional": {"image": ("IMAGE",), "mask": ("MASK",)},
@@ -87,72 +91,124 @@ class MTB_ExtractCoordinatesFromImage:
         image: torch.Tensor | None = None,
         mask: torch.Tensor | None = None,
     ) -> tuple[list[list[tuple[int, int]]], torch.Tensor]:
-        if image is not None:
-            batch_count, height, width, channel_count = image.shape
-            imgs = image
-        else:
-            if mask is None:
-                raise ValueError("Must provide either image or mask")
-            batch_count, height, width = mask.shape
-            channel_count = 1
-            imgs = mask
+        if image is None and mask is None:
+            raise ValueError("Must provide either image or mask")
 
-        if channel_count not in [1, 2, 3, 4]:
-            raise ValueError(f"Incorrect channel count: {channel_count}")
+        if image is not None:
+            batch_count, height, width, _channel_count = image.shape
+            input_device = image.device
+            if mask is not None:
+                if mask.ndim == 2:
+                    mask = mask.unsqueeze(0)
+                if mask.ndim != 3:
+                    raise ValueError(
+                        f"Mask has unexpected ndim: {mask.ndim}. Expected 2 or 3."
+                    )
+
+                b_mask, h_mask, w_mask = mask.shape
+                if not (h_mask == height and w_mask == width):
+                    raise ValueError(
+                        f"Image dimensions ({height}x{width}) and mask dimensions ({h_mask}x{w_mask}) are spatially incompatible."
+                    )
+                if b_mask == 1 and batch_count > 1:
+                    mask = mask.expand(batch_count, height, width)
+
+                elif b_mask != batch_count:
+                    raise ValueError(
+                        f"Image batch size ({batch_count}) and mask batch size ({b_mask}) are incompatible and mask cannot be broadcast."
+                    )
+        else:
+            if mask.ndim == 2:
+                mask = mask.unsqueeze(0)
+
+            if mask.ndim != 3:
+                raise ValueError(
+                    f"Mask has unexpected ndim: {mask.ndim} when image is not provided. Expected 2 or 3."
+                )
+
+            batch_count, height, width = mask.shape
+            input_device = mask.device
 
         all_points: list[list[tuple[int, int]]] = []
         debug_images = torch.zeros(
             (batch_count, height, width, 3),
             dtype=torch.uint8,
-            device=imgs.device,
+            device=input_device,
         )
 
-        for i, img in enumerate(imgs):
-            if channel_count == 1:
-                alpha_channel = img if len(img.shape) == 2 else img[:, :, 0]
-            elif channel_count == 2:
-                alpha_channel = img[:, :, 1]
-            elif channel_count == 4:
-                alpha_channel = img[:, :, 3]
+        points_tensor = torch.tensor(
+            [255, 255, 255], dtype=torch.uint8, device=input_device
+        )
+
+        for i in range(batch_count):
+            value_threshold: torch.Tensor
+            if image is not None:
+                img_slice = image[i]
+                img_channels = img_slice.shape[2]
+                if img_channels == 1 or img_channels == 2:
+                    value_threshold = img_slice[:, :, 0]
+                elif img_channels == 3 or img_channels == 4:
+                    value_threshold = img_slice[:, :, :3].max(dim=2)[0]
+                else:
+                    raise ValueError(
+                        f"Unsupported image channel count: {img_channels} for image at batch index {i}"
+                    )
             else:
-                # get intensity
-                alpha_channel = img[:, :, :3].max(dim=2)[0]
+                mask_slice = mask[i]
+                value_threshold = mask_slice
 
-            points = (alpha_channel > threshold).nonzero(as_tuple=False)
+            condition = value_threshold > threshold
+            if image is not None and mask is not None:
+                mask_slice = mask[i]
+                mask_active_condition = mask_slice > 0.0
+                condition = condition & mask_active_condition
 
-            if len(points) > max_points:
-                indices = torch.randperm(points.size(0), device=img.device)[
-                    :max_points
-                ]
-                points = points[indices]
+            points_yx = condition.nonzero(as_tuple=False)
 
-            points = [(int(y.item()), int(x.item())) for x, y in points]
-            all_points.append(points)
+            if points_yx.size(0) > max_points:
+                # shuffle and pick max_points randomly
+                indices = torch.randperm(
+                    points_yx.size(0), device=input_device
+                )[:max_points]
+                points_yx = points_yx[indices]
+            elif max_points == 0:
+                points_yx = torch.empty(
+                    (0, 2), dtype=torch.long, device=input_device
+                )
 
-            for x, y in points:
-                self._draw_circle(debug_images[i], (x, y), 5)
+            current_points = [
+                (int(p[1].item()), int(p[0].item())) for p in points_yx
+            ]
+            all_points.append(current_points)
+            for x_coord, y_coord in current_points:
+                self._draw_circle(
+                    debug_images[i],
+                    (x_coord, y_coord),
+                    radius=5,
+                    color_tensor=points_tensor,
+                )
 
         return (all_points, debug_images)
 
     @staticmethod
     def _draw_circle(
-        image: torch.Tensor, center: tuple[int, int], radius: int
+        image: torch.Tensor,
+        center: tuple[int, int],
+        radius: int,
+        color_tensor: torch.Tensor,
     ):
         """Draw a 5px circle on the image."""
         x0, y0 = center
-        for x in range(-radius, radius + 1):
-            for y in range(-radius, radius + 1):
-                in_radius = x**2 + y**2 <= radius**2
-                in_bounds = (
-                    0 <= x0 + x < image.shape[1]
-                    and 0 <= y0 + y < image.shape[0]
-                )
-                if in_radius and in_bounds:
-                    image[y0 + y, x0 + x] = torch.tensor(
-                        [255, 255, 255],
-                        dtype=torch.uint8,
-                        device=image.device,
-                    )
+        h, w, _ = image.shape
+        min_x_bbox = max(0, x0 - radius)
+        max_x_bbox = min(w - 1, x0 + radius)
+        min_y_bbox = max(0, y0 - radius)
+        max_y_bbox = min(h - 1, y0 + radius)
+
+        for py in range(min_y_bbox, max_y_bbox + 1):
+            for px in range(min_x_bbox, max_x_bbox + 1):
+                if (px - x0) ** 2 + (py - y0) ** 2 <= radius**2:
+                    image[py, px] = color_tensor
 
 
 class MTB_ColorCorrectGPU:
@@ -627,6 +683,7 @@ class MTB_ImageCompare:
 
 
 import requests
+import time
 
 
 class MTB_LoadImageFromUrl:
@@ -642,6 +699,14 @@ class MTB_LoadImageFromUrl:
                         "default": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Example.jpg/800px-Example.jpg"
                     },
                 ),
+                "retry_count": (
+                    "INT",
+                    {"default": 3, "min": 1, "max": 20, "step": 1},
+                ),
+                "retry_interval": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 60.0, "step": 0.1},
+                ),
             }
         }
 
@@ -649,11 +714,27 @@ class MTB_LoadImageFromUrl:
     FUNCTION = "load"
     CATEGORY = "mtb/IO"
 
-    def load(self, url):
-        # get the image from the url
-        image = Image.open(requests.get(url, stream=True).raw)
-        image = ImageOps.exif_transpose(image)
-        return (pil2tensor(image),)
+    def load(self, url, retry_count, retry_interval):
+        # get the image from the url with retry + exponential backoff
+        last_error = None
+        for attempt in range(retry_count):
+            try:
+                response = requests.get(url, stream=True)
+                response.raise_for_status()
+                image = Image.open(response.raw)
+                image = ImageOps.exif_transpose(image)
+                return (pil2tensor(image),)
+            except Exception as e:
+                last_error = e
+                if attempt == retry_count - 1:
+                    raise
+                wait_seconds = retry_interval * (2**attempt)
+                if wait_seconds > 0:
+                    time.sleep(wait_seconds)
+
+        if last_error is not None:
+            raise last_error
+        raise RuntimeError("Failed to load image from URL without captured exception")
 
 
 class MTB_Blur:
@@ -823,8 +904,11 @@ class MTB_MaskToImage:
         return {
             "required": {
                 "mask": ("MASK",),
-                "color": ("COLOR",),
-                "background": ("COLOR", {"default": "#000000"}),
+                "color": ("COLOR", {"widgetType": "MTB_COLOR"}),
+                "background": (
+                    "COLOR",
+                    {"default": "#000000", "widgetType": "MTB_COLOR"},
+                ),
             },
             "optional": {
                 "invert": ("BOOLEAN", {"default": False}),

nodes/image_utils.py

@@ -21,7 +21,11 @@ class MTB_StackImages:
                 "match_method": (
                     ["error", "smallest", "largest"],
                     {"default": "error"},
-                )
+                ),
+                "output_rgb": (
+                    "BOOLEAN",
+                    {"default": True, "tooltip": "Output RGB instead of RGBA"},
+                ),
             },
         }
 
@@ -29,7 +33,7 @@ class MTB_StackImages:
     FUNCTION = "stack"
     CATEGORY = "mtb/image utils"
 
-    def stack(self, vertical, match_method="error", **kwargs):
+    def stack(self, vertical, match_method="error", output_rgb=True, **kwargs):
         if not kwargs:
             raise ValueError("At least one tensor must be provided.")
 
@@ -98,6 +102,9 @@ class MTB_StackImages:
 
         stacked_tensor = torch.cat(normalized_tensors, dim=dim)
 
+        if output_rgb:
+            stacked_tensor = stacked_tensor[:, :, :, :3]
+
         return (stacked_tensor,)
 
     def normalize_to_rgba(self, tensor):

nodes/legacy.py

@@ -0,0 +1,17 @@
+# from ..utils import hex_to_rgb
+class MTB_ColorInput:
+    RETURN_TYPES = ("COLOR",)
+    FUNCTION = "color"
+    CATEGORY = "mtb/color"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"color": ("MTB_COLOR", {"default": "#ffffff"})},
+        }
+
+    def color(self, color):
+        return (color,)
+
+
+__nodes__ = [MTB_ColorInput]

nodes/mask.py

@@ -34,7 +34,7 @@ class MTB_ImageRemoveBackgroundRembg:
                 ),
                 "bgcolor": (
                     "COLOR",
-                    {"default": "#000000"},
+                    {"default": "#000000","widgetType": "MTB_COLOR"},
                 ),
             },
         }

nodes/model.py

@@ -145,7 +145,7 @@ class MTB_ModelPatchSeamless:
         tilingX,
         tilingY,
     ):
-        hacked_model = copy.deepcopy(model)
+        hacked_model = model.clone()
         self.apply_circular(
             hacked_model.model, startStep, stopStep, tilingX, tilingY
         )

nodes/transform.py

@@ -43,7 +43,10 @@ class MTB_TransformImage:
                     ["edge", "constant", "reflect", "symmetric"],
                     {"default": "edge"},
                 ),
-                "constant_color": ("COLOR", {"default": "#000000"}),
+                "constant_color": (
+                    "COLOR",
+                    {"default": "#000000", "widgetType": "MTB_COLOR"},
+                ),
             },
             "optional": {
                 "filter_type": (

nodes/vitmatte.py

@@ -27,7 +27,7 @@ class MTB_LoadVitMatteModel:
     def execute(self, *, kind: str, autodownload: bool):
         dest = models_dir / "vitmatte"
         dest.mkdir(exist_ok=True)
-        name = "dist" if kind == "Distinctions-646" else "com"
+        name = "dis" if kind == "Distinctions-646" else "com"
 
         file = hf_hub_download(
             repo_id="melmass/pytorch-scripts",

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "comfy-mtb"
-version = "0.3.0"
+version = "0.5.4"
 description = "Animation oriented nodes pack for ComfyUI."
 license = { text = "MIT" }
 readme = "README.md"
@@ -63,7 +63,7 @@ DisplayName = "comfy-mtb"
 Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
 
 [tool.bumpversion]
-current_version = "0.3.0"
+current_version = "0.5.1"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 search = "{current_version}"

utils.py

@@ -1,6 +1,5 @@
 import contextlib
 import functools
-import importlib
 import math
 import operator
 import os
@@ -462,23 +461,6 @@ def _run_command(shell_cmd, ignored_lines_start):
     print("Command executed successfully!")
 
 
-def import_install(package_name):
-    package_spec = reqs_map.get(package_name, package_name)
-
-    try:
-        importlib.import_module(package_name)
-
-    except Exception:  # (ImportError, ModuleNotFoundError):
-        run_command(
-            [
-                Path(sys.executable).as_posix(),
-                "-m",
-                "pip",
-                "install",
-                package_spec,
-            ]
-        )
-        importlib.import_module(package_name)
 
 
 # endregion

web/comfy_shared.js

@@ -25,6 +25,19 @@ export function makeUUID() {
   return uuid
 }
 
+// - basic debounce decorator
+export function debounce(func, delay) {
+  let timeout
+  let debounced = function (...args) {
+    clearTimeout(timeout)
+    timeout = setTimeout(() => func.apply(this, args), delay)
+  }
+  debounced.cancel = () => {
+    clearTimeout(timeout)
+  }
+  return debounced
+}
+
 //- local storage manager
 export class LocalStorageManager {
   constructor(namespace) {
@@ -195,6 +208,7 @@ export function hideWidgetForGood(node, widget, suffix = '') {
   widget.origComputeSize = widget.computeSize
   widget.origSerializeValue = widget.serializeValue
   widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
+  widget.hidden = true
   widget.type = CONVERTED_TYPE + suffix
   // widget.serializeValue = () => {
   //     // Prevent serializing the widget if we have no input linked
@@ -621,21 +635,21 @@ function getBrightness(rgbObj) {
 export function calculateTotalChildrenHeight(parentElement) {
   let totalHeight = 0
 
+  if (!parentElement || !parentElement.children) {
+    return 0
+  }
+
   for (const child of parentElement.children) {
     const style = window.getComputedStyle(child)
 
-    // Get height as an integer (without 'px')
-    const height = Number.parseInt(style.height, 10)
+    const height = Number.parseFloat(style.height)
+    const marginTop = Number.parseFloat(style.marginTop)
+    const marginBottom = Number.parseFloat(style.marginBottom)
 
-    // Get vertical margin as integers
-    const marginTop = Number.parseInt(style.marginTop, 10)
-    const marginBottom = Number.parseInt(style.marginBottom, 10)
-
-    // Sum up height and vertical margins
     totalHeight += height + marginTop + marginBottom
   }
 
-  return totalHeight
+  return Math.ceil(totalHeight)
 }
 
 export const loadScript = (
@@ -646,13 +660,15 @@ export const loadScript = (
   return new Promise((resolve, reject) => {
     try {
       // Check if the script already exists
-      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
-      if (existingScript) {
-        resolve({ status: true, message: 'Script already loaded' })
+      let scriptEle = document.querySelector(`script[src="${FILE_URL}"]`)
+      if (scriptEle) {
+        scriptEle.addEventListener('load', (_ev) => {
+          resolve({ status: true })
+        })
         return
       }
 
-      const scriptEle = document.createElement('script')
+      scriptEle = document.createElement('script')
       scriptEle.type = type
       scriptEle.async = async
       scriptEle.src = FILE_URL
@@ -671,6 +687,8 @@ export const loadScript = (
       document.body.appendChild(scriptEle)
     } catch (error) {
       reject(error)
+    } finally {
+      infoLogger(`Finally loaded script: ${FILE_URL}`)
     }
   })
 }
@@ -784,12 +802,10 @@ function loadParser(shiki) {
 
 export const ensureMarkdownParser = async (callback) => {
   infoLogger('Ensuring md parser')
-  let use_shiki = false
-  try {
-    use_shiki = await api.getSetting('mtb.Use Shiki')
-  } catch (e) {
-    console.warn('Option not available yet', e)
-  }
+  const use_shiki = app.extensionManager.setting.get(
+    'mtb.noteplus.use-shiki',
+    false,
+  )
 
   if (window.MTB?.mdParser) {
     infoLogger('Markdown parser found')
@@ -814,8 +830,7 @@ export const ensureMarkdownParser = async (callback) => {
     callbackQueue.push(callback)
   }
 
-  await parserPromise
-  await parserPromise
+  await await parserPromise
 
   return window.MTB.mdParser
 }
@@ -1154,58 +1169,3 @@ export const setServerInfo = async (opts) => {
 }
 
 // #endregion
-
-// #region Authoring API / graph utilities
-export const getAPIInputs = () => {
-  const inputs = {}
-  let counter = 1
-  for (const node of getNodes(true)) {
-    const widgets = node.widgets
-
-    if (node.properties.mtb_api && node.properties.useAPI) {
-      if (node.properties.mtb_api.inputs) {
-        for (const currentName in node.properties.mtb_api.inputs) {
-          const current = node.properties.mtb_api.inputs[currentName]
-          if (current.enabled) {
-            const inputName = current.name || currentName
-            const widget = widgets.find((w) => w.name === currentName)
-            if (!widget) continue
-            if (!(inputName in inputs)) {
-              inputs[inputName] = {
-                ...current,
-                id: counter,
-                name: inputName,
-                type: current.type,
-                node_id: node.id,
-                widgets: [],
-              }
-            }
-            inputs[inputName].widgets.push(widget)
-            counter = counter + 1
-          }
-        }
-      }
-    }
-  }
-  return inputs
-}
-
-export const getNodes = (skip_unused) => {
-  const nodes = []
-  for (const outerNode of app.graph.computeExecutionOrder(false)) {
-    const skipNode =
-      (outerNode.mode === 2 || outerNode.mode === 4) && skip_unused
-    const innerNodes =
-      !skipNode && outerNode.getInnerNodes
-        ? outerNode.getInnerNodes()
-        : [outerNode]
-    for (const node of innerNodes) {
-      if ((node.mode === 2 || node.mode === 4) && skip_unused) {
-        continue
-      }
-      nodes.push(node)
-    }
-  }
-  return nodes
-}
-// #endregion

web/debug.js

@@ -28,7 +28,7 @@ function createDebugSection(title) {
     margin: '8px 0',
     padding: '8px',
     borderRadius: '4px',
-    backgroundColor: 'rgba(0,0,0,0.2)'
+    backgroundColor: 'rgba(0,0,0,0.2)',
   })
 
   const header = mtb_ui.makeElement('h3', {
@@ -37,7 +37,7 @@ function createDebugSection(title) {
     borderBottom: '1px solid rgba(255,255,255,0.1)',
     fontSize: '14px',
     fontWeight: 'bold',
-    color: '#9f9'
+    color: '#9f9',
   })
   header.textContent = title
   section.appendChild(header)
@@ -47,21 +47,21 @@ function createDebugSection(title) {
 
 function createDebugContent(content, type) {
   const wrapper = mtb_ui.makeElement('div', {
-    margin: '4px 0'
+    margin: '4px 0',
   })
 
   if (type === 'text') {
     const text = mtb_ui.makeElement('p', {
       margin: '2px 0',
       fontFamily: 'monospace',
-      whiteSpace: 'pre-wrap'
+      whiteSpace: 'pre-wrap',
     })
     text.innerHTML = content
     wrapper.appendChild(text)
   } else if (type === 'image') {
     const img = mtb_ui.makeElement('img', {
       width: '100%',
-      borderRadius: '2px'
+      borderRadius: '2px',
     })
     img.src = content
     wrapper.appendChild(img)
@@ -148,18 +148,18 @@ app.registerExtension({
         const uiData = data.ui || data
 
         if (uiData.items) {
-            uiData.items.forEach(item => {
-                const inputName = item.input
-                if (!inputData[inputName]) {
-                    inputData[inputName] = { text: [], b64_images: [] }
-                }
-                if (item.text) {
-                    inputData[inputName].text.push(...item.text)
-                }
-                if (item.b64_images) {
-                    inputData[inputName].b64_images.push(...item.b64_images)
-                }
-            })
+          uiData.items.forEach((item) => {
+            const inputName = item.input
+            if (!inputData[inputName]) {
+              inputData[inputName] = { text: [], b64_images: [] }
+            }
+            if (item.text) {
+              inputData[inputName].text.push(...item.text)
+            }
+            if (item.b64_images) {
+              inputData[inputName].b64_images.push(...item.b64_images)
+            }
+          })
         }
 
         let widgetI = 1
@@ -171,23 +171,18 @@ app.registerExtension({
           const section = createDebugSection(inputName)
 
           if (content.text.length > 0) {
-            content.text.forEach(text => {
+            content.text.forEach((text) => {
               section.appendChild(createDebugContent(text, 'text'))
             })
           }
 
           if (content.b64_images.length > 0) {
-            content.b64_images.forEach(img => {
+            content.b64_images.forEach((img) => {
               section.appendChild(createDebugContent(img, 'image'))
             })
           }
 
-          this.addDOMWidget(
-            `debug_section_${widgetI}`,
-            'CUSTOM',
-            section,
-            {}
-          )
+          this.addDOMWidget(`debug_section_${widgetI}`, 'CUSTOM', section, {})
           widgetI++
         }
 

web/imageFeed.js

@@ -13,40 +13,40 @@ import { api } from '../../scripts/api.js'
 import { app } from '../../scripts/app.js'
 import { LocalStorageManager } from './comfy_shared.js'
 const styles = {
-	lighbox: {
-		position: 'fixed',
-		top: 0,
-		left: 0,
-		width: '100vw',
-		height: '100vh',
-		background: 'rgba(0,0,0,0.5)',
-		display: 'none',
-		justifyContent: 'center',
-		alignItems: 'center',
-		zIndex: 999,
-	},
-	lightboxBtn: (extra) => ({
-		position: 'absolute',
-		top: '50%',
-		background: 'none',
-		border: 'none',
-		color: '#fff',
-		zIndex: 1000,
-		fontSize: '30px',
-		cursor: 'pointer',
-		pointerEvents: 'auto',
-		...extra,
-	}),
-	img_list: {
-		minHeight: '30px',
-		maxHeight: '300px',
-		width: '100vw',
-		position: 'absolute',
-		bottom: 0,
-		zIndex: 10,
-		background: '#333',
-		overflow: 'auto',
-	},
+  lighbox: {
+    position: 'fixed',
+    top: 0,
+    left: 0,
+    width: '100vw',
+    height: '100vh',
+    background: 'rgba(0,0,0,0.5)',
+    display: 'none',
+    justifyContent: 'center',
+    alignItems: 'center',
+    zIndex: 999,
+  },
+  lightboxBtn: (extra) => ({
+    position: 'absolute',
+    top: '50%',
+    background: 'none',
+    border: 'none',
+    color: '#fff',
+    zIndex: 1000,
+    fontSize: '30px',
+    cursor: 'pointer',
+    pointerEvents: 'auto',
+    ...extra,
+  }),
+  img_list: {
+    minHeight: '30px',
+    maxHeight: '300px',
+    width: '100vw',
+    position: 'absolute',
+    bottom: 0,
+    zIndex: 10,
+    background: '#333',
+    overflow: 'auto',
+  },
 }
 
 let currentImageIndex = 0
@@ -58,299 +58,299 @@ const storage = new LocalStorageManager('mtb')
 let activated = storage.get('image_feed', false)
 
 app.registerExtension({
-	name: 'mtb.ImageFeed',
-	setup: () => {
-		app.ui.settings.addSetting({
-			id: 'mtb.Main.image-feed-enabled',
-			category: ['mtb', 'Main', 'image-feed-enabled'],
-			name: 'Enable Image Feed',
-			type: 'boolean',
-			defaultValue: false,
-			attrs: {
-				style: {
-					fontFamily: 'monospace',
-				},
-			},
-			async onChange(value) {
-				storage.set('image_feed', value)
-				activated = value
-			},
-		})
-	},
-	init: async () => {
-		if (!activated) {
-			return
-		}
-		const pythongossFeed = app.extensions.find(
-			(e) => e.name === 'pysssss.ImageFeed',
-		)
-		if (pythongossFeed) {
-			console.warn(
-				"[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
-			)
-			activated = false // just in case other methods are added later on
-			return
-		}
-		// - HTML & CSS
-		//- lightbox
-		const lightboxContainer = document.createElement('div')
-		Object.assign(lightboxContainer.style, styles.lighbox)
+  name: 'mtb.ImageFeed',
+  setup: () => {
+    app.ui.settings.addSetting({
+      id: 'mtb.Main.image-feed-enabled',
+      category: ['mtb', ' Main', 'image-feed-enabled'],
+      name: 'Enable Image Feed',
+      type: 'boolean',
+      defaultValue: false,
+      attrs: {
+        style: {
+          fontFamily: 'monospace',
+        },
+      },
+      async onChange(value) {
+        storage.set('image_feed', value)
+        activated = value
+      },
+    })
+  },
+  init: async () => {
+    if (!activated) {
+      return
+    }
+    const pythongossFeed = app.extensions.find(
+      (e) => e.name === 'pysssss.ImageFeed',
+    )
+    if (pythongossFeed) {
+      console.warn(
+        "[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
+      )
+      activated = false // just in case other methods are added later on
+      return
+    }
+    // - HTML & CSS
+    //- lightbox
+    const lightboxContainer = document.createElement('div')
+    Object.assign(lightboxContainer.style, styles.lighbox)
 
-		const lightboxImage = document.createElement('img')
-		Object.assign(lightboxImage.style, {
-			maxHeight: '100%',
-			maxWidth: '100%',
-			borderRadius: '5px',
-		})
+    const lightboxImage = document.createElement('img')
+    Object.assign(lightboxImage.style, {
+      maxHeight: '100%',
+      maxWidth: '100%',
+      borderRadius: '5px',
+    })
 
-		// previous and next buttons
-		const lightboxPrevBtn = document.createElement('button')
-		const lightboxNextBtn = document.createElement('button')
+    // previous and next buttons
+    const lightboxPrevBtn = document.createElement('button')
+    const lightboxNextBtn = document.createElement('button')
 
-		lightboxPrevBtn.textContent = '❮'
-		lightboxNextBtn.textContent = '❯'
+    lightboxPrevBtn.textContent = '❮'
+    lightboxNextBtn.textContent = '❯'
 
-		Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
-		Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
+    Object.assign(lightboxPrevBtn.style, styles.lightboxBtn({ left: '0%' }))
+    Object.assign(lightboxNextBtn.style, styles.lightboxBtn({ right: '0%' }))
 
-		// close button
-		const lightboxCloseBtn = document.createElement('button')
-		Object.assign(
-			lightboxCloseBtn.style,
-			styles.lightboxBtn({ right: '0', top: '0' }),
-		)
-		lightboxCloseBtn.textContent = '❌'
+    // close button
+    const lightboxCloseBtn = document.createElement('button')
+    Object.assign(
+      lightboxCloseBtn.style,
+      styles.lightboxBtn({ right: '0', top: '0' }),
+    )
+    lightboxCloseBtn.textContent = '❌'
 
-		const lightboxButtons = document.createElement('div')
-		Object.assign(lightboxButtons.style, {
-			position: 'absolute',
-			top: '0%',
-			right: '0%',
-			// transform: "translate(50%, -50%)",
-			height: '100%',
-			width: '100%',
-			background: 'none',
-			border: 'none',
-			color: '#fff',
-			fontSize: '30px',
-			cursor: 'pointer',
-			pointerEvents: 'none',
-		})
+    const lightboxButtons = document.createElement('div')
+    Object.assign(lightboxButtons.style, {
+      position: 'absolute',
+      top: '0%',
+      right: '0%',
+      // transform: "translate(50%, -50%)",
+      height: '100%',
+      width: '100%',
+      background: 'none',
+      border: 'none',
+      color: '#fff',
+      fontSize: '30px',
+      cursor: 'pointer',
+      pointerEvents: 'none',
+    })
 
-		lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
-		lightboxContainer.append(lightboxButtons, lightboxImage)
+    lightboxButtons.append(lightboxPrevBtn, lightboxNextBtn, lightboxCloseBtn)
+    lightboxContainer.append(lightboxButtons, lightboxImage)
 
-		//- image list
-		const imageListContainer = document.createElement('div')
-		Object.assign(imageListContainer.style, styles.img_list)
+    //- image list
+    const imageListContainer = document.createElement('div')
+    Object.assign(imageListContainer.style, styles.img_list)
 
-		const createImgListBtn = (text, style) => {
-			const btn = document.createElement('button')
-			btn.type = 'button'
-			btn.textContent = text
-			Object.assign(btn.style, {
-				...style,
-				border: 'none',
-				color: '#fff',
-				background: 'none',
-				height: '20px',
-				cursor: 'pointer',
-				position: 'absolute',
-				top: '5px',
-				fontSize: '12px',
-				lineHeight: '12px',
-			})
-			imageListContainer.append(btn)
-			return btn
-		}
-		const showBtn = document.createElement('button')
-		const closeBtn = createImgListBtn('❌', {
-			width: '20px',
-			textIndent: '-4px',
-			right: '5px',
-		})
-		const loadButton = createImgListBtn('Load Session History', {
-			right: '90px',
-		})
-		const clearButton = createImgListBtn('Clear', {
-			right: '30px',
-		})
+    const createImgListBtn = (text, style) => {
+      const btn = document.createElement('button')
+      btn.type = 'button'
+      btn.textContent = text
+      Object.assign(btn.style, {
+        ...style,
+        border: 'none',
+        color: '#fff',
+        background: 'none',
+        height: '20px',
+        cursor: 'pointer',
+        position: 'absolute',
+        top: '5px',
+        fontSize: '12px',
+        lineHeight: '12px',
+      })
+      imageListContainer.append(btn)
+      return btn
+    }
+    const showBtn = document.createElement('button')
+    const closeBtn = createImgListBtn('❌', {
+      width: '20px',
+      textIndent: '-4px',
+      right: '5px',
+    })
+    const loadButton = createImgListBtn('Load Session History', {
+      right: '90px',
+    })
+    const clearButton = createImgListBtn('Clear', {
+      right: '30px',
+    })
 
-		//- tools popup button
-		showBtn.classList.add('comfy-settings-btn')
-		Object.assign(showBtn.style, {
-			right: '16px',
-			cursor: 'pointer',
-			display: 'none',
-		})
+    //- tools popup button
+    showBtn.classList.add('comfy-settings-btn')
+    Object.assign(showBtn.style, {
+      right: '16px',
+      cursor: 'pointer',
+      display: 'none',
+    })
 
-		//- append to DOM
-		document.body.append(imageListContainer)
+    //- append to DOM
+    document.body.append(imageListContainer)
 
-		showBtn.textContent = '🖼'
-		showBtn.onclick = () => {
-			imageListContainer.style.display = 'block'
-			showBtn.style.display = 'none'
-		}
-		document.querySelector('.comfy-settings-btn').after(showBtn)
-		document.querySelector('.comfy-settings-btn').after(lightboxContainer)
+    showBtn.textContent = '🖼'
+    showBtn.onclick = () => {
+      imageListContainer.style.display = 'block'
+      showBtn.style.display = 'none'
+    }
+    document.querySelector('.comfy-settings-btn').after(showBtn)
+    document.querySelector('.comfy-settings-btn').after(lightboxContainer)
 
-		// for (const { output } of history) {
-		// 	if (output?.images) {
-		// 		for (const src of output.images) {
-		// 			const img = document.createElement("img");
-		// 			const but = document.createElement("button");
+    // for (const { output } of history) {
+    // 	if (output?.images) {
+    // 		for (const src of output.images) {
+    // 			const img = document.createElement("img");
+    // 			const but = document.createElement("button");
 
-		//- callbacks
-		closeBtn.onclick = () => {
-			imageListContainer.style.display = 'none'
-			showBtn.style.display = 'unset'
-		}
+    //- callbacks
+    closeBtn.onclick = () => {
+      imageListContainer.style.display = 'none'
+      showBtn.style.display = 'unset'
+    }
 
-		clearButton.onclick = () => {
-			imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
-		}
+    clearButton.onclick = () => {
+      imageListContainer.replaceChildren(closeBtn, clearButton, loadButton)
+    }
 
-		lightboxNextBtn.onclick = () => {
-			currentImageIndex = (currentImageIndex + 1) % imageUrls.length
-			const imageUrl = imageUrls[currentImageIndex]
-			lightboxImage.src = imageUrl
-		}
+    lightboxNextBtn.onclick = () => {
+      currentImageIndex = (currentImageIndex + 1) % imageUrls.length
+      const imageUrl = imageUrls[currentImageIndex]
+      lightboxImage.src = imageUrl
+    }
 
-		// Modify the lightboxPrevBtn onclick callback
-		lightboxPrevBtn.onclick = () => {
-			currentImageIndex =
-				(currentImageIndex - 1 + imageUrls.length) % imageUrls.length
-			const imageUrl = imageUrls[currentImageIndex]
-			lightboxImage.src = imageUrl
-		}
+    // Modify the lightboxPrevBtn onclick callback
+    lightboxPrevBtn.onclick = () => {
+      currentImageIndex =
+        (currentImageIndex - 1 + imageUrls.length) % imageUrls.length
+      const imageUrl = imageUrls[currentImageIndex]
+      lightboxImage.src = imageUrl
+    }
 
-		lightboxCloseBtn.onclick = () => {
-			lightboxContainer.style.display = 'none'
-		}
-		lightboxImage.onclick = lightboxNextBtn.onclick
-		/**
-		 * This is the function that creates the image buttons for the image list
-		 * They are wrapped in a button so that they can be clicked and open
-		 * the image in the lightbox.
-		 * @param {*} src
-		 */
-		const createImageBtn = (src) => {
-			console.debug(`making image ${src.filename}`)
-			const img = document.createElement('img')
-			const but = document.createElement('button')
+    lightboxCloseBtn.onclick = () => {
+      lightboxContainer.style.display = 'none'
+    }
+    lightboxImage.onclick = lightboxNextBtn.onclick
+    /**
+     * This is the function that creates the image buttons for the image list
+     * They are wrapped in a button so that they can be clicked and open
+     * the image in the lightbox.
+     * @param {*} src
+     */
+    const createImageBtn = (src) => {
+      console.debug(`making image ${src.filename}`)
+      const img = document.createElement('img')
+      const but = document.createElement('button')
 
-			Object.assign(but.style, {
-				height: '120px',
-				width: '120px',
-				border: 'none',
-				padding: 0,
-				margin: 0,
-			})
-			Object.assign(img.style, {
-				width: '100%',
-				height: '100%',
-				objectFit: 'cover',
-			})
+      Object.assign(but.style, {
+        height: '120px',
+        width: '120px',
+        border: 'none',
+        padding: 0,
+        margin: 0,
+      })
+      Object.assign(img.style, {
+        width: '100%',
+        height: '100%',
+        objectFit: 'cover',
+      })
 
-			img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
-				src.type
-			}&subfolder=${encodeURIComponent(src.subfolder)}`
+      img.src = `/view?filename=${encodeURIComponent(src.filename)}&type=${
+        src.type
+      }&subfolder=${encodeURIComponent(src.subfolder)}`
 
-			imageUrls.push(img.src)
+      imageUrls.push(img.src)
 
-			console.debug(img.src)
+      console.debug(img.src)
 
-			img.onload = () => {
-				but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
-			}
+      img.onload = () => {
+        but.style.width = `${120 * (img.naturalWidth / img.naturalHeight)}px`
+      }
 
-			but.onclick = () => {
-				lightboxContainer.style.display = 'flex'
-				// add the same image to the lightbox
-				lightboxImage.src = img.src
-				// lighboxContainer.replaceChildren(lightboxButtons, img);
-			}
+      but.onclick = () => {
+        lightboxContainer.style.display = 'flex'
+        // add the same image to the lightbox
+        lightboxImage.src = img.src
+        // lighboxContainer.replaceChildren(lightboxButtons, img);
+      }
 
-			// add right click menu
-			but.addEventListener('contextmenu', (e) => {
-				e.preventDefault()
+      // add right click menu
+      but.addEventListener('contextmenu', (e) => {
+        e.preventDefault()
 
-				if (image_menu) {
-					image_menu.remove()
-				}
+        if (image_menu) {
+          image_menu.remove()
+        }
 
-				image_menu = document.createElement('div')
-				Object.assign(image_menu.style, {
-					position: 'absolute',
-					top: `${e.clientY}px`,
-					left: `${e.clientX}px`,
-					background: '#333',
-					color: '#fff',
-					padding: '5px',
-					borderRadius: '5px',
-					zIndex: 999,
-				})
-				const load_img = document.createElement('button')
-				load_img.textContent = 'Load'
-				load_img.onclick = () => {
-					app.handleFile(img.src)
-				}
+        image_menu = document.createElement('div')
+        Object.assign(image_menu.style, {
+          position: 'absolute',
+          top: `${e.clientY}px`,
+          left: `${e.clientX}px`,
+          background: '#333',
+          color: '#fff',
+          padding: '5px',
+          borderRadius: '5px',
+          zIndex: 999,
+        })
+        const load_img = document.createElement('button')
+        load_img.textContent = 'Load'
+        load_img.onclick = () => {
+          app.handleFile(img.src)
+        }
 
-				image_menu.appendChild(load_img)
-				document.body.appendChild(image_menu)
-			})
+        image_menu.appendChild(load_img)
+        document.body.appendChild(image_menu)
+      })
 
-			but.append(img)
-			imageListContainer.prepend(but)
-		}
+      but.append(img)
+      imageListContainer.prepend(but)
+    }
 
-		loadButton.onclick = async () => {
-			const all_history = await api.getHistory()
-			for (const history of all_history.History) {
-				if (history.outputs) {
-					for (const key of Object.keys(history.outputs)) {
-						console.debug(key)
-						if (history.outputs[key].images) {
-							for (const im of history.outputs[key].images) {
-								console.debug(im)
-								createImageBtn(im)
-							}
-						}
-					}
-					// for (const src of outputs.outputs.images) {
-					// 	console.debug(src)
-					// 	makeImage(`${src.subfolder}/${src.filename}`)
-					// }
-				}
-			}
-		}
+    loadButton.onclick = async () => {
+      const all_history = await api.getHistory()
+      for (const history of all_history.History) {
+        if (history.outputs) {
+          for (const key of Object.keys(history.outputs)) {
+            console.debug(key)
+            if (history.outputs[key].images) {
+              for (const im of history.outputs[key].images) {
+                console.debug(im)
+                createImageBtn(im)
+              }
+            }
+          }
+          // for (const src of outputs.outputs.images) {
+          // 	console.debug(src)
+          // 	makeImage(`${src.subfolder}/${src.filename}`)
+          // }
+        }
+      }
+    }
 
-		///////-------
+    ///////-------
 
-		// const all_history = await api.getHistory()
-		// for (const history of all_history.History) {
-		// 	if (history.outputs) {
-		// 		for (const key of Object.keys(history.outputs)) {
-		// 			for (const im of history.outputs[key].images) {
-		// 				makeImage(im)
-		// 			}
-		// 		}
-		// 		// for (const src of outputs.outputs.images) {
-		// 		// 	console.debug(src)
-		// 		// 	makeImage(`${src.subfolder}/${src.filename}`)
-		// 		// }
-		// 	}
-		// }
+    // const all_history = await api.getHistory()
+    // for (const history of all_history.History) {
+    // 	if (history.outputs) {
+    // 		for (const key of Object.keys(history.outputs)) {
+    // 			for (const im of history.outputs[key].images) {
+    // 				makeImage(im)
+    // 			}
+    // 		}
+    // 		// for (const src of outputs.outputs.images) {
+    // 		// 	console.debug(src)
+    // 		// 	makeImage(`${src.subfolder}/${src.filename}`)
+    // 		// }
+    // 	}
+    // }
 
-		//- Hook into the API
-		api.addEventListener('executed', ({ detail }) => {
-			if (detail?.output?.images) {
-				for (const src of detail.output.images) {
-					console.debug(`Adding ${src} to image feed`)
-					createImageBtn(src)
-				}
-			}
-		})
-	},
+    //- Hook into the API
+    api.addEventListener('executed', ({ detail }) => {
+      if (detail?.output?.images) {
+        for (const src of detail.output.images) {
+          console.debug(`Adding ${src} to image feed`)
+          createImageBtn(src)
+        }
+      }
+    })
+  },
 })

web/mtb_input_output_sidebar.js

@@ -3,6 +3,7 @@
 import { app } from '../../scripts/app.js'
 import { api } from '../../scripts/api.js'
 
+import * as mtb_ui from './mtb_ui.js'
 import * as shared from './comfy_shared.js'
 
 import {
@@ -15,7 +16,13 @@ import {
 } from './mtb_ui.js'
 
 const offset = 0
+
+// These are "global" variables mostly meant to sync user settings.
 let currentWidth = 200
+let saltUrls =
+  app.extensionManager.setting.get('mtb.io-sidebar.salt_urls') || false
+let targetWidth =
+  app.extensionManager.setting.get('mtb.io-sidebar.img-size') || 512
 let currentMode = 'input'
 let subfolder = ''
 let currentSort = 'None'
@@ -46,15 +53,19 @@ const updateImage = (node, image) => {
  * @param {ResultItem} resultItem
  * @returns {string} - The request URL.
  */
-const resultItemToQuery = (resultItem) =>
-  [
+const resultItemToQuery = (resultItem) => {
+  const res = [
     `/mtb/view?filename=${resultItem.filename}`,
-    `width=512`,
     `type=${resultItem.type}`,
     `subfolder=${resultItem.subfolder}`,
-    `preview=`,
-  ].join('&')
+    'preview=',
+  ]
+  if (targetWidth > 0) {
+    res.splice(1, 0, `width=${targetWidth}`)
+  }
 
+  return res.join('&')
+}
 /**
  * Retrieves the unique prompt ID from a history task item.
  * @param {HistoryTaskItem} historyTaskItem
@@ -80,7 +91,7 @@ const getNewOutputUrls = (mostRecentTask) => {
       const imageOutputs = Object.values(nodeOutputs.images)
       imageOutputs.forEach(
         (resultItem) =>
-          (urls[resultItem.filename] = resultItemToQuery(resultItem))
+          (urls[resultItem.filename] = resultItemToQuery(resultItem)),
       )
     }
     // Can process `animated` and `audio` outputs here.
@@ -209,7 +220,7 @@ const getUrls = async (subfolder) => {
   if (currentMode === 'video') {
     const output = await shared.runAction(
       'getUserVideos',
-      256,
+      targetWidth,
       count,
       offset,
       currentSort,
@@ -219,11 +230,13 @@ const getUrls = async (subfolder) => {
   const output = await shared.runAction(
     'getUserImages',
     currentMode,
+    targetWidth,
     count,
     offset,
     currentSort,
     false,
     subfolder,
+    saltUrls,
   )
   return output || {}
 }
@@ -236,55 +249,110 @@ if (window?.__COMFYUI_FRONTEND_VERSION__) {
 
   const sidebar_extension = {
     name: 'mtb.io-sidebar',
-    // init: async () => {
-    //   try {
-    //     const res = await api.fetchApi('/mtb/server-info')
-    //     const msg = await res.json()
-    //     exposed = msg.exposed
-    //   } catch (e) {
-    //     console.error('Error:', e)
-    //   }
-    // },
-    init: () => {
-      let handle
-      const version = window?.__COMFYUI_FRONTEND_VERSION__
-      console.log(`%c ${version}`, 'background: orange; color: white;')
-
-      ensureMTBStyles()
-
-      app.ui.settings.addSetting({
+    settings: [
+      {
         id: 'mtb.io-sidebar.count',
         category: ['mtb', 'Input & Output Sidebar', 'count'],
-
         name: 'Number of images to fetch',
         type: 'number',
         defaultValue: 1000,
-
         tooltip:
           "This setting affects the input/output sidebar to determine how many images to fetch per pagination (pagination is not yet supported so for now it's the static total)",
-        attrs: {
-          style: {
-            // fontFamily: 'monospace',
-          },
+      },
+      {
+        id: 'mtb.io-sidebar.salt_urls',
+        category: ['mtb', 'Input & Output Sidebar', 'salt_urls'],
+        name: 'Salt URLs',
+        type: 'boolean',
+        defaultValue: false,
+        onChange: (n, o) => {
+          saltUrls = n
         },
-      })
-
-      app.ui.settings.addSetting({
+        tooltip:
+          'Adds a random query parameter to every urls to always invalidate caching.',
+      },
+      {
         id: 'mtb.io-sidebar.img-size',
         category: ['mtb', 'Input & Output Sidebar', 'img-size'],
 
-        name: 'Resolution of the images',
-        type: 'number',
+        name: 'Resize width of shown images',
         defaultValue: 512,
+        type: (name, setter, value, attrs) => {
+          targetWidth = value
+          const container = mtb_ui.makeElement('div', {
+            display: 'flex',
+            alignItems: 'center',
+            gap: '8px',
+          })
 
-        tooltip: "It's recommended to keep it at 512px",
-        attrs: {
-          style: {
-            // fontFamily: 'monospace',
-          },
+          console.log({ name, setter, value, attrs })
+
+          const baseId = name.replace(/[^a-zA-Z0-9]/g, '-').toLowerCase()
+          const checkboxId = `${baseId}-checkbox`
+          const numberInputId = `${baseId}-number`
+
+          const isCheckedInitially = value !== -1
+
+          // TODO: better way to get defaultValue?
+          const defaultValue = 512
+          const initialNumberValue = isCheckedInitially ? value : defaultValue
+
+          console.log('recreate')
+          const checkbox = mtb_ui.makeElement(
+            // harder to match styles (.p-toggleswitch-input)
+            // since it uses a div synced to the input...
+            'input',
+            {},
+            container,
+          )
+          checkbox.type = 'checkbox'
+          checkbox.id = checkboxId
+          checkbox.checked = isCheckedInitially
+
+          const numberInput = mtb_ui.makeElement(
+            'input.p-inputtext',
+            {},
+            container,
+          )
+          numberInput.type = 'number'
+          numberInput.id = numberInputId
+          numberInput.value = initialNumberValue
+          numberInput.disabled = !isCheckedInitially
+          numberInput.min = 128
+
+          checkbox.addEventListener('change', () => {
+            let valToSet = -1
+            if (checkbox.checked) {
+              numberInput.disabled = false
+
+              valToSet = Number.parseInt(numberInput.value, 10)
+              if (Number.isNaN(valToSet) || valToSet < numberInput.min) {
+                valToSet = defaultValue
+                numberInput.value = valToSet
+              }
+            } else {
+              numberInput.disabled = true
+            }
+            setter(valToSet)
+          })
+
+          numberInput.addEventListener('input', () => {
+            if (checkbox.checked) {
+              const numValue = Number.parseInt(numberInput.value, 10)
+              if (!Number.isNaN(numValue) && numberInput.value !== '') {
+                setter(numValue)
+              }
+            }
+          })
+
+          return container
         },
-      })
-      app.ui.settings.addSetting({
+
+        tooltip:
+          "If browsing large folders it's recommended to use this to avoid overflow/crash of the webpage. Image will get resized to this target width on the server before being sent to the client.",
+      },
+
+      {
         id: 'mtb.io-sidebar.sort',
         category: ['mtb', 'Input & Output Sidebar', 'sort'],
         name: 'Default sort mode',
@@ -304,7 +372,39 @@ if (window?.__COMFYUI_FRONTEND_VERSION__) {
           'Name',
           'Name-Reverse',
         ],
-      })
+      },
+      {
+        id: 'mtb.io-sidebar.notice',
+        category: ['mtb', 'Input & Output Sidebar', 'sort'],
+        name: ' ',
+
+        type: (name, setter, value, attrs) => {
+          const container = mtb_ui.makeElement('div')
+          const notice =
+            '## Important\nIf you make **any** edits here you need to toggle off and back on the sidebar for it to take effect.'
+
+          if (window.MTB?.mdParser) {
+            MTB.mdParser.parse(notice).then((e) => {
+              container.innerHTML = e
+            })
+          } else {
+            shared.ensureMarkdownParser((p) => {
+              p.parse(notice).then((e) => {
+                container.innerHTML = e
+              })
+            })
+          }
+          return container
+        },
+      },
+    ],
+
+    init: () => {
+      let handle
+      const version = window?.__COMFYUI_FRONTEND_VERSION__
+      console.log(`%c ${version}`, 'background: orange; color: white;')
+
+      ensureMTBStyles()
 
       app.extensionManager.registerSidebarTab({
         id: 'mtb-inputs-outputs',

web/mtb_sidebar.js

@@ -1,28 +0,0 @@
-// NOTE: this will be the LT part of mtb API system
-// I need to properly publish the source and fix a few things before
-
-// import { app } from '../../scripts/app.js'
-// // import { api } from '../../scripts/api.js'
-//
-// import * as shared from './comfy_shared.js'
-// import { createOutliner } from './dist/mtb_inspector.js'
-//
-// if (window?.__COMFYUI_FRONTEND_VERSION__) {
-//   const version = window?.__COMFYUI_FRONTEND_VERSION__
-//   console.log(`%c ${version}`, 'background: orange; color: white;')
-//
-//   const panel = app.extensionManager.registerSidebarTab({
-//     id: 'mtb-nodes',
-//     icon: 'pi pi-bolt',
-//     title: 'MTB',
-//     tooltip: 'MTB: API outliner',
-//     type: 'custom',
-//     // this is run everytime the tab's diplay is toggled on.
-//     render: (el) => {
-//       const outliner = createOutliner(el)
-//       const inputs = shared.getAPIInputs()
-//       console.log('INPUTS', inputs)
-//       outliner.$$set({ inputs })
-//     },
-//   })
-// }

web/mtb_ui.js

@@ -203,7 +203,7 @@ export const wrapElement = (element, style = {}) => {
  * @param {Object} [style] - CSS styles to apply to the element.
  * @returns {HTMLElement} - The created DOM element.
  */
-export const makeElement = (kind, style) => {
+export const makeElement = (kind, style, parent) => {
   let [real_kind, className] = kind.split('.')
   let id
 
@@ -224,6 +224,9 @@ export const makeElement = (kind, style) => {
   if (id) {
     el.id = id
   }
+  if (parent) {
+    parent.appendChild(el)
+  }
 
   return el
 }

web/mtb_widgets.js

@@ -21,7 +21,7 @@ import { infoLogger } from './comfy_shared.js'
 import { NumberInputWidget } from './numberInput.js'
 
 // NOTE: new widget types registered by MTB Widgets
-const newTypes = [/*'BOOL'*/ 'COLOR', 'BBOX']
+const newTypes = [/*'BOOL'*/ 'COLOR','MTB_COLOR', 'BBOX']
 
 const deprecated_nodes = {
   //  'Animation Builder':
@@ -694,7 +694,7 @@ const mtb_widgets = {
 
     app.ui.settings.addSetting({
       id: 'mtb.Main.debug-enabled',
-      category: ['mtb', 'Main', 'debug-enabled'],
+      category: ['mtb', ' Main', 'debug-enabled'],
       name: 'Enable Debug (py and js)',
       type: 'boolean',
       defaultValue: false,
@@ -739,7 +739,6 @@ const mtb_widgets = {
       // },
 
       COLOR: (node, inputName, inputData, _app) => {
-        console.debug('Registering color')
         return {
           widget: node.addCustomWidget(
             MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000'),
@@ -748,6 +747,16 @@ const mtb_widgets = {
           minHeight: 30,
         }
       },
+      MTB_COLOR: (node, inputName, inputData, _app) => {
+        return {
+          widget: node.addCustomWidget(
+            MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000'),
+          ),
+          minWidth: 150,
+          minHeight: 30,
+        }
+      },
+
       // BBOX: (node, inputName, inputData, app) => {
       //     console.debug("Registering bbox")
       //     return {
@@ -1012,12 +1021,15 @@ const mtb_widgets = {
           )
           loop_preview.value = 'Iteration: Idle'
 
+          let cancelQueue = false
+
           const onReset = () => {
             raw_iteration.value = 0
             raw_loop.value = 0
 
             value_preview.value = 'Idle'
             loop_preview.value = 'Iteration: Idle'
+            cancelQueue = false
 
             app.canvas.setDirty(true)
           }
@@ -1026,15 +1038,43 @@ const mtb_widgets = {
           this.addWidget('button', 'Reset', 'reset', onReset)
 
           // run button
-          this.addWidget('button', 'Queue', 'queue', () => {
-            onReset() // this could maybe be a setting or checkbox
-            app.queuePrompt(0, total_frames.value * loop_count.value)
+          const chunkSize = 10
+          this.addWidget('button', 'Queue', 'queue', async () => {
+            onReset()
+
+            const totalPrompts = total_frames.value * loop_count.value
             window.MTB?.notify?.(
-              `Started a queue of ${total_frames.value} frames (for ${
-                loop_count.value
-              } loop, so ${total_frames.value * loop_count.value})`,
+              `Starting a queue of ${totalPrompts} frames in chunks of ${chunkSize}...`,
               5000,
             )
+
+            for (let i = 0; i < totalPrompts; i += chunkSize) {
+              console.log({ cancelQueue })
+              if (cancelQueue) {
+                window.MTB?.notify?.(
+                  `Queueing cancelled after ${i} frames.`,
+                  3000,
+                )
+                break
+              }
+              const currentChunkSize = Math.min(chunkSize, totalPrompts - i)
+
+              await app.queuePrompt(0, currentChunkSize)
+
+            }
+            if (!cancelQueue) {
+              window.MTB?.notify?.(
+                `Finished queuing ${totalPrompts} frames.`,
+                5000,
+              )
+            }
+          })
+          this.addWidget('button', 'Cancel', 'cancel', () => {
+            cancelQueue = true
+            window.MTB?.notify?.(
+              'Cancellation requested. Waiting for current chunk to finish...',
+              3000,
+            )
           })
 
           this.onRemoved = () => {

web/note_plus.constants.js

@@ -1,10 +1,13 @@
 // web/note_plus.constants.js
 
-export const DEFAULT_CSS = ''
+export const DEFAULT_CSS = `/** here you can write css**/
+h1 {
+    color: whitesmoke;
+}`
 export const DEFAULT_HTML = `<p style='color:red;font-family:monospace'>
     Note+
 </p>`
-export const DEFAULT_MD = '## Note+'
+export const DEFAULT_MD = '# 📝 Note+'
 export const DEFAULT_MODE = 'markdown'
 export const DEFAULT_THEME = 'one_dark'
 
@@ -55,58 +58,57 @@ We also support github callout:
 `
 
 export const THEMES = [
-	'ambiance',
-	'chaos',
-	'chrome',
-	'cloud9_day',
-	'cloud9_night',
-	'cloud9_night_low_color',
-	'cloud_editor',
-	'cloud_editor_dark',
-	'clouds',
-	'clouds_midnight',
-	'cobalt',
-	'crimson_editor',
-	'dawn',
-	'dracula',
-	'dreamweaver',
-	'eclipse',
-	'github',
-	'github_dark',
-	'gob',
-	'gruvbox',
-	'gruvbox_dark_hard',
-	'gruvbox_light_hard',
-	'idle_fingers',
-	'iplastic',
-	'katzenmilch',
-	'kr_theme',
-	'kuroir',
-	'merbivore',
-	'merbivore_soft',
-	'mono_industrial',
-	'monokai',
-	'nord_dark',
-	'one_dark',
-	'pastel_on_dark',
-	'solarized_dark',
-	'solarized_light',
-	'sqlserver',
-	'terminal',
-	'textmate',
-	'tomorrow',
-	'tomorrow_night',
-	'tomorrow_night_blue',
-	'tomorrow_night_bright',
-	'tomorrow_night_eighties',
-	'twilight',
-	'vibrant_ink',
-	'vscode',
+  'ambiance',
+  'chaos',
+  'chrome',
+  'cloud9_day',
+  'cloud9_night',
+  'cloud9_night_low_color',
+  'cloud_editor',
+  'cloud_editor_dark',
+  'clouds',
+  'clouds_midnight',
+  'cobalt',
+  'crimson_editor',
+  'dawn',
+  'dracula',
+  'dreamweaver',
+  'eclipse',
+  'github',
+  'github_dark',
+  'gob',
+  'gruvbox',
+  'gruvbox_dark_hard',
+  'gruvbox_light_hard',
+  'idle_fingers',
+  'iplastic',
+  'katzenmilch',
+  'kr_theme',
+  'kuroir',
+  'merbivore',
+  'merbivore_soft',
+  'mono_industrial',
+  'monokai',
+  'nord_dark',
+  'one_dark',
+  'pastel_on_dark',
+  'solarized_dark',
+  'solarized_light',
+  'sqlserver',
+  'terminal',
+  'textmate',
+  'tomorrow',
+  'tomorrow_night',
+  'tomorrow_night_blue',
+  'tomorrow_night_bright',
+  'tomorrow_night_eighties',
+  'twilight',
+  'vibrant_ink',
+  'vscode',
 ]
 
 export const CSS_RESET = `
 * {
-  font-family: monospace;
   line-height: 1.25em;
 }
 .shiki{
@@ -116,6 +118,8 @@ export const CSS_RESET = `
 .markdown-callout-title {
     .octicon{
         fill:white;
+        width:29px;
+        height:29px;
     }
 	/* background: var(--current-color); */
 	color: var(--current-color);
@@ -124,6 +128,8 @@ export const CSS_RESET = `
 	/* border-start-start-radius: var(--radius); */
 	padding: 0.5em;
 	padding-inline-start: 1em;
+  display: flex;
+  align-items: center;
 }
 .markdown-callout-content {
 	padding: 1em;
@@ -136,7 +142,12 @@ export const CSS_RESET = `
 	border-left: 3px solid var(--current-color);
 	margin-bottom: 1em;
 	margin-top: 1em;
+
 }
+.markdown-callout p:nth-child(2) {
+    padding:1em;
+  }
+
 
 .markdown-callout-tip {
 	--text-color: whitesmoke;
@@ -164,8 +175,9 @@ export const CSS_RESET = `
   flex-direction:column;
   align-items: flex-start;
   width:95%;
-  margin-left: 20px;
-  margin-top:20px;
+  /*margin-left: 20px;*/
+  /*margin-top:20px;*/
+
   /*background-color: rgba(255,0,0,0.5)!important;*/
 }
 

web/notify.js

@@ -41,7 +41,16 @@ const toastStyle = `
     transition-duration: ${transition_time}ms;
   `
 
-function notify(message, timeout = 3000) {
+function notify(message, timeout = 3000, old_mode = false) {
+  if (!old_mode) {
+    app.extensionManager.toast.add({
+      severity: 'info',
+      summary: 'MTB',
+      detail: message,
+      life: timeout,
+    })
+    return
+  }
   log('Creating toast')
   const container = document.getElementById('mtb-notify-container')
   const toast = document.createElement('div')
@@ -59,7 +68,7 @@ function notify(message, timeout = 3000) {
       log('Transition out')
       const totalHeight = Array.from(container.children).reduce(
         (acc, child) => acc + child.offsetHeight + 10, // Add spacing of 10px between toasts
-        0
+        0,
       )
       container.style.height = `${totalHeight}px`
 
@@ -83,7 +92,7 @@ function notify(message, timeout = 3000) {
   // Update container's height to fit new toast
   const totalHeight = Array.from(container.children).reduce(
     (acc, child) => acc + child.offsetHeight + 10, // Add spacing of 10px between toasts
-    0
+    0,
   )
   container.style.height = `${totalHeight}px`